Better the Two Devils You Know, Than the One You Don’t: Predictability Influences Moral Judgments of Immoral Actors
Bibliographic record
Abstract
Across six studies (N = 2,646), we demonstrate the role that perceptions of predictability play in judgments of moral character, finding that people demonstrate a moral preference for more predictable immoral actors. Participants judged agents performing an immoral action (e.g., assault) for an unintelligible reason as less predictable and less moral than agents performing the same immoral action, along with an additional immoral action (e.g., theft), for a well-understood immoral reason (Studies 1-4). Additionally, agents performing an immoral action for an unintelligible reason were judged as less predictable and less moral compared to agents performing the same immoral act for an unstated reason (Studies 3-5). This moral preference persisted when participants viewed video footage of each agent’s immoral action (Study 5). Finally, agents performing immoral actions in an unusual way were judged as less predictable and less moral than those performing the same actions in a more common manner (Study 6). The present research demonstrates how immoral actions performed without a clear motive or in an unpredictable way are perceived to be especially indicative of poor moral character. In revealing peoples’ moral preference for predictable immoral actors, we propose that perceptions of predictability play an important, yet overlooked, role in judgments of moral character. Furthermore, we propose that predictability influences judgments of moral character for its ultimate role in reducing social uncertainty and facilitating cooperation with trustworthy individuals and discuss how these findings may be accommodated by person-centered theories of moral judgment and theories of morality-as-cooperation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".